Single-cell profiling of physically interacting immune cells using image-guided sorting
- 1La Jolla Institute for Immunology
Abstract
Single-cell transcriptomics of cell-cell complexes offers key insights into in vivo immune interactions, yet distinguishing true physical contacts from artifacts remains challenging. Here, we integrated spectral imaging cell sorting with physical cell deconvolution to separate circulating T cell-monocyte doublets from tuberculosis patients into physically interacting (in-contact) and non-interacting (coincidental) populations. Single-cell sequencing of individual cell partners revealed that physical contact is associated with robust signatures distinct from singlets, including unidirectional monocyte-to-T cell RNA transfer, heightened metabolic activity and T cell cytotoxicity, and a trend toward antigen-specific enrichment. These signatures spanned all T cell and monocyte subpopulations, reflecting a global program of T cell-monocyte engagement. Coincidental doublets displayed intermediate transcriptional profiles between singlet and in-contact states, suggesting the presence of recently disrupted or forming physical pairs. This framework provides high-fidelity deconvolution of cellular interactions, uncovering signatures governing T cell-monocyte communication in human disease.
Related
- Transcriptome profiling identifies the chromatoid body as a dynamic center for RNA surveillance and transcript sorting during spermatogenesis
- Single-cell DNA cytometry with magnetic- and fluorescence-activated bead sorting
- Cutting-edge microfluidic technology allows detection and sorting of single fluorescent extracellular vesicles
- A Modular Chromosomally Integrated Yeast Surface Display Platform for Quantitative Serology and Immune Profiling
- Immune profiling of anti-SSA–positive at-risk individuals reveals increased type I IFN activity preceding lymphocyte abnormalities in Sjögren’s disease
BibTeX
@misc{kang2026singlecell,
title = {{Single-cell profiling of physically interacting immune cells using image-guided sorting}},
author = {N. Kang and C. Kim and T. J. Scriba and B. Peters and J. G. Burel},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.09.758094},
url = {https://www.biorxiv.org/content/10.64898/2026.10.09.758094v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Single-cell profiling of physically interacting immune cells using image-guided sorting
AU - N. Kang
AU - C. Kim
AU - T. J. Scriba
AU - B. Peters
AU - J. G. Burel
PY - 2026
DA - 2026/10/09
PB - bioRxiv
DO - 10.64898/2026.10.09.758094
UR - https://www.biorxiv.org/content/10.64898/2026.10.09.758094v1
AB - Single-cell transcriptomics of cell-cell complexes offers key insights into in vivo immune interactions, yet distinguishing true physical contacts from artifacts remains challenging. Here, we integrated spectral imaging cell sorting with physical cell deconvolution to separate circulating T cell-monocyte doublets from tuberculosis patients into physically interacting (in-contact) and non-interacting (coincidental) populations. Single-cell sequencing of individual cell partners revealed that physical contact is associated with robust signatures distinct from singlets, including unidirectional monocyte-to-T cell RNA transfer, heightened metabolic activity and T cell cytotoxicity, and a trend toward antigen-specific enrichment. These signatures spanned all T cell and monocyte subpopulations, reflecting a global program of T cell-monocyte engagement. Coincidental doublets displayed intermediate transcriptional profiles between singlet and in-contact states, suggesting the presence of recently disrupted or forming physical pairs. This framework provides high-fidelity deconvolution of cellular interactions, uncovering signatures governing T cell-monocyte communication in human disease.
ER -